加工车间和批处理车间协同调度的模型构建与算法实现

发布时间：2018-02-01 12:36

本文关键词： 加工车间批处理车间协同调度智能算法决策优化　出处：《西北民族大学》2017年硕士论文　论文类型：学位论文

【摘要】：在制造企业生产过程中,有效的车间作业调度方法和优化技术的研究与应用,对提高生产效率、降低生产成本等方面起着至关重要的作用。随着"中国制造2025"和"工业4.0时代"战略的提出,对车间作业调度的方法和技术提出了高标准的要求。研究Job Shop调度和批处理协同调度的优化方法,对于制造企业控制产能过剩逐渐实现零库存等目标具有重要的理论价值和实践意义。本文首先介绍了车间调度问题的概念、分类和特点以及当前国内外研究现状,并结合车间作业实际环境情况分析了车间调度存在的问题和研究的意义;概述了遗传算法和粒子群算法的基本原理和流程,介绍了遗传算法常用的遗传操作算子,分析了遗传算法的特点。其次是基于多工艺约束条件分别建立了 Job Shop调度和批处理的数学模型,对Job Shop调度与批处理协同调度提出了两种方法,并进行了讨论。研究了 Job Shop调度和批处理串行协同调度的问题,以优化生产周期为目标建立了基于多工艺加工计划的串行协同生产调度集成框架图和数学模型,给出了 lingo算法。通过实例仿真,研究了串行协同方法,证明该方法性能一般,其生产周期是Job Shop调度和批处理各自生产周期之和;研究了 Job Shop调度和批处理并行协同调度的问题,以优化生产周期为目标,建立了基于多工艺加工计划的并行协同生产调度集成框架图和数学模型,给出了求解Job Shop调度问题的遗传算法和求解批处理问题的粒子群算法。通过实例仿真,研究了并行协同方法,证明该方法能够取得较佳的调度性能。且生产周期比串行协同调度方法得到的生产周期明显缩短。最后,综合研究结果并以企业生产实践为背景开发了生产调度系统。
[Abstract]:In the production process of manufacturing enterprises, the research and application of effective job shop scheduling method and optimization technology can improve the production efficiency. Reducing production cost plays an important role. With the "made in China 2025" and "Industrial 4.0 era" strategy proposed. The methods and techniques of job shop scheduling are proposed, and the optimization methods of Job Shop scheduling and batch cooperative scheduling are studied. It has important theoretical value and practical significance for manufacturing enterprises to control overcapacity and gradually achieve zero inventory. Firstly, this paper introduces the concept of job shop scheduling problem. Classification and characteristics as well as the current research situation at home and abroad, and combined with the actual environment of the job shop to analyze the existing problems and research significance of job shop scheduling; This paper summarizes the basic principle and flow of genetic algorithm and particle swarm optimization algorithm, and introduces the common genetic operator of genetic algorithm. The characteristics of genetic algorithm are analyzed. Secondly, the mathematical models of Job Shop scheduling and batch processing are established based on multi-process constraints. This paper presents two methods of Job Shop scheduling and batch collaborative scheduling, and discusses the problems of Job Shop scheduling and batch serial collaborative scheduling. In order to optimize the production cycle, the integrated framework diagram and mathematical model of serial collaborative production scheduling based on multi-process process planning are established, and the lingo algorithm is given. The serial collaboration method is studied, and it is proved that the performance of this method is general, and its production cycle is the sum of the respective production cycles of Job Shop scheduling and batch processing. The problem of Job Shop scheduling and batch parallel collaborative scheduling is studied, with the aim of optimizing production cycle. The integrated framework diagram and mathematical model of parallel collaborative production scheduling based on multi-process planning are established. The genetic algorithm for solving Job Shop scheduling problem and particle swarm optimization algorithm for solving batch problem are presented. It is proved that this method can achieve better scheduling performance and the production cycle is shorter than that obtained by serial collaborative scheduling method. Finally. A production scheduling system is developed based on the research results and production practice.
【学位授予单位】：西北民族大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TB497;TP18

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